Alan Anwer Abdulla
University of Buckingham
26 Papers
150 Citations
Alan Anwer Abdulla is an academic researcher from University of Buckingham. The author has contributed to research in topics: Steganography & Computer science. The author has an hindex of 9, co-authored 22 publications. Previous affiliations of Alan Anwer Abdulla include Information Technology University & Cork College of Commerce.
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Papers
•Dissertation
Exploiting similarities between secret and cover images for improved embedding efficiency and security in digital steganography
Alan Anwer Abdulla
- 01 Oct 2015
TL;DR: This thesis is devoted to investigate and develop steganography schemes for embedding secret images in image files with optimal performance in terms of imperceptibility of the hidden secrets, payload capacity, efficiency of embedding and robustness against steganalysis attacks.
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Improving embedding efficiency for digital steganography by exploiting similarities between secret and cover images
TL;DR: This paper will demonstrate that this strategy produces stego-images that have minimal distortion, high embedding efficiency, reasonably good stEGo-image quality and robustness against 3 well-known targeted steganalysis tools.
74
Stego Quality Enhancement by Message Size Reduction and Fibonacci Bit-Plane Mapping
Alan Anwer Abdulla,Harin Sellahewa,Sabah Jassim +2 more
- 16 Dec 2014
TL;DR: In this paper, an efficient 2-step steganography technique is proposed to enhance stego image quality and secret message un-detectability, which is achieved by using bit-plane(s) mapping instead of bitplane replacement for embedding.
An efficient CAD system for ALL cell identification from microscopic blood images
TL;DR: This paper concerns with developing an efficient automatic system for the identification of acute lymphoblastic leukemia (ALL) cells by segmenting the white blood cells into normal and abnormal cells.
53
Thresholding-based White Blood Cells Segmentation from Microscopic Blood Images
Zhana Fidakar Mohammed,Alan Anwer Abdulla +1 more
- 13 Feb 2020
TL;DR: Experimental results demonstrate that the proposed thresholding-based segmentation technique provides better results compared to color-k-means clustering technique for segmenting WBCs as well as the time consumption of the proposed technique is less than the color- k-Means which are 70.8144 ms and 204.7188 ms, respectively.